the contribution of behavioral economics to political science

PL14CH10-Wilson ARI 11 April 2011 12:24

The Contributionof Behavioral Economicsto Political ScienceRick K. WilsonDepartment of Political Science, Rice University, Houston, Texas 77251-1892;email: [email protected]

Annu. Rev. Polit. Sci. 2011. 14:201–23

First published online as a Review in Advance onMarch 17, 2011

The Annual Review of Political Science is online atpolisci.annualreviews.org

This article’s doi:10.1146/annurev-polisci-041309-114513

Copyright c© 2011 by Annual Reviews.All rights reserved

1094-2939/11/0615-0201$20.00

Keywords

experimental economics, social preferences, time discounting,prospect theory, behavioral anomalies

Abstract

Behavioral economics has become an important part of the economicsprofession. As a subfield, it tries to make sense of persistent violations ofthe standard model for economics. The major classes of violations in-volve social preferences (taking the well-being of others into account),time discounting (inconsistencies in valuing present and future com-modities), and context (the effects of framing). Other violations in-volve well-known psychological heuristics such as overconfidence, con-straints on strategic reasoning, emotions, and status differentials. Theseconcepts are discussed in separate sections, and key experimental andempirical studies are noted.

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INTRODUCTIONMy long-time coauthor Catherine Eckel (whois an economist) tells me that “behavioraleconomics” is all about bringing humans backinto economics. By humans she means at-tributes of people that are due to human geneticmake-up, are embedded in our psychology,and are part of the long-term socializationprocesses. Betsy Hoffman (an economist whois a mutual friend), in a like-mannered way,characterized game theory as a wonderfultheory of autism in which individuals thinkdeeply and strategically about how they wouldplay perfectly foresighted rational actors likethemselves. But this means game theory isan analytic model of computationally intro-spective individuals who give little thoughtto the social world. Elinor Ostrom, in herPresidential Address to the American PoliticalScience Association in 1997, cautioned theprofession about elegant models that failedto make use of new findings in genetics andneuroscience, which pointed to systematichuman biases leading to out-of-equilibriumpredictions. She called on the profession to usesuch findings to better inform our theoreticalmodels (Ostrom 1998). All these women makethe case that to understand social behavior weneed to account for social beings.

Behavioral economics has made enormousinroads into economics over the past twodecades. It has established itself in the pro-fession and has evolved a great deal sinceCamerer’s (1997) call for action. Today thereare popular books on the topic (Akerlof& Shiller 2009, Thaler & Sunstein 2009),businesses consult behavioral (and neuro-)economists to determine product placement,the Obama administration has appointedone well-known advocate (Cass Sunstein) asAdministrator of the White House Office ofInformation and Regulatory Affairs, and thejournals are filled with research under titlescontaining “behavioral economics.”

Twenty years ago it would have beenpossible to thoroughly survey this growingfield. Ten years ago the field was beginningto boom and a thorough survey would have

been difficult. Today the field is enormous andI make no claims at giving it full treatment.Instead I focus on several topics that in whichbehavioral economics can claim successes—topics for which there have been breakthroughsand that have spawned new research areas. Atthe same time, I have selected the topics thatwill most resonate with areas of interest forpolitical scientists. What I do not want to dois leave the impression that standard rationalchoice or game theoretic models have beensupplanted. They have not. Such approachesto modeling strategic behavior remain valuableand provide considerable insight. As Ostrom(1998) notes, these models perform well whenthere are many actors (e.g., markets, elections),and where they begin to break down is whenaspects of human behavior are not accountedfor by the simplifying assumptions of our mod-els. This means we need a different modelingstrategy, not a rejection of modeling itself.

What properly constitutes behavioral eco-nomics is disputed. As in any growing field, di-visions appear and seemingly fine distinctionsare made. Khalil (2009), in his introduction toa three-volume collection of classic papers onbehavioral economics, differentiates betweenthose who are tinkering with the “objectivefunction” (making assumptions about prefer-ences) and those who are inserting heuristicsinto models. The former tackles the questionof preferences directly. The latter harkens backto an older tradition of bounded rationality inwhich actors face cognitive costs that can behandled in much the same way as moving abudget constraint for an actor with fixed utilityfunctions. In this sense, the division is betweenthose who are trying to fundamentally revisethe standard toolbox and those who are simplyimporting psychology into economics.

For those wanting to delve deeper intothe topic, Advances in Behavioral Economics(Camerer et al. 2003) is a good place to start.This collection of 18 articles, originally pub-lished elsewhere, sketches the landscape of be-havioral economics. In conjunction with thebook, it is instructive to read its reviews, as theydetail the concerns held by the profession (see

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particularly Harrison 2005, Fudenberg 2006,and Pesendorfer 2006). For those wanting topush even further, there is the three-volumecollection of articles by Khalil (2009). Thereare also numerous overviews of the literaturethat are geared for economists (see for exampleCamerer 1997 or DellaVigna 2009).

Generally, three major topics catch the at-tention of behavioral economists: social pref-erences, time discounting, and context. Theseare at the core of understanding preferences,and in this review I spend some time on each. Afourth topic pertains to psychological heuristicsand has also been prominent in behavioral eco-nomics. Much of what has driven research onall of these topics comes from the experimen-tal economics tradition, and this is what I bestknow. Throughout my discussion, I point toclassic experiments that drove subsequent work,and I provide examples of new research that iscarrying forward the work.

SOCIAL PREFERENCES

A rich area of research has focused on “other-regarding preferences.” This work in eco-nomics has made inroads into political science,so I spend a great deal of this article discussingit. Standard game theoretic models start withaxioms long ago presented by Luce & Raiffa(1957) (among others) stating that interper-sonal comparisons are irrelevant in strategicchoice. This seemed reasonable in that actorsshould care about their own utility and not thatof their opponent in a game. This made mostproblems tractable. Yet a set of canonical ex-periments showed that subjects pay attentionto something other than their own earnings,and this presented an increasingly interestingpuzzle for theorists. I discuss four canonicalgames: the ultimatum game, the dictator game,the trust game, and the public goods game.(For excellent surveys of these canonical games,see the review articles in Kagel & Roth 1995,Camerer 2003, and a forthcoming second edi-tion of Kagel & Roth 1995.) In this article, Ipoint out several basic findings and what thesemeant for other-regarding preferences. I then

return to a variety of models that have been pro-posed to add consideration of other people intostandard utility theory.

The Ultimatum Game

The ultimatum game is a very simple two-person bargaining game. Player A is givenan amount of money to split with player B.Player A announces the split, and B can acceptor reject the proposed division. If accepted,the division is implemented and both partiesgo on their way. If rejected, both parties getnothing. The division is proposed only once(an ultimatum) and both parties know this.The theory is straightforward. In equilibrium,A will offer the smallest possible amount. B willaccept whatever is offered, since any positiveamount is preferred to zero. No positiveamount proposed by A should ever be rejected.

In the first experiment to test this con-cept, Guth et al. (1982) found two peculiarthings. First, almost all subjects gave more thanthe minimum—indeed, in the first experiment,the modal offer was 50% of the endowment.Second, nonzero offers were rejected. In asecond experiment, subjects played the rolesof both the proposer (A) and the responder(B). Subjects were first asked what they wouldpropose and then they were asked what theywould minimally accept. Again subjects pro-posed much more than the minimum (on av-erage 45% of the endowment). Once again re-sponders were willing to reject nonzero offers,with the median amount they were willing toaccept being almost 36% of the endowment.Thaler (1988), in one of a number of influen-tial articles that appeared under “Anomalies”in the Journal of Economic Perspectives, broughtthe game into the mainstream. The results werenot warmly received. Economists and theoristssuggested that the stakes were trivial, subjectsdid not understand the problem, the instruc-tions were misleading, and the student samplewas hardly of relevance. However, these resultsgenerated an enormous number of experimentsdesigned to pick apart various explanations forthe behavior. Some interpreted these results as

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indicating that proposers were concerned withwhat the responder might do. In particular,was the proposer interested in a fair divisionor afraid of being rejected? Was the respon-der concerned with fairness or concerned withbeing insulted?

Since this time, hundreds of experimentshave tried to understand behavior in the ulti-matum game. Blount (1995) demonstrated thatthe intentions of the proposer matter. In her ex-periment, subjects were paired with proposerswho were either humans or a computer (andwere told which). She showed that low offersfrom humans were rejected, but not low offersfrom a computer. Cameron (1999) looked at theeffect of stakes and showed that even very largestakes (up to three times an individual’s monthlyincome) did not markedly change proposer be-havior, although responders were more likely toaccept offers. Eckel & Grossman (2001) foundthat the gender composition of pairings makesa difference, with males more likely to acceptlower offers from women (chivalry) and womenmore likely to accept lower offers from women(solidarity). Roth et al. (1991) pointed tocross-cultural differences among students, andHenrich et al. (2001) found cross-cultural varia-tion among people in small, primitive societies.Bahry & Wilson (2006) showed that strongnorms can persist in societies, with offers above50% being rejected. Sanfey et al. (2003) demon-strated neurological correlates with behavior inthe ultimatum game, with additional support byKoenigs & Tranel (2007), who used the gameon subjects with lesion damage to the ventro-medial prefrontal cortex. By and large, theseultimatum game results, in which unexpectedlylarge offers are made and low offers are rejected,persist.

Political scientists have not ignored the ul-timatum game. Hibbing & Alford (2004) haveused it to demonstrate that individuals are will-ing to cooperate when they feel that they arenot likely to be “taken for a sucker.” If theprocess awarding the proposer an advantage isconsidered fair, low offers are accepted. If theproposer’s intention is clear (and consideredfair) then low offers are also accepted. Smith

(2006) used the same idea of “wary coopera-tors” and looked at proposers who were putinto a position where they could act for them-selves or on behalf of others (mimicking a ver-sion of representation). He found that there isa propensity to act on behalf of others in theultimatum game. Bahry & Wilson (2004) usedthe ultimatum game to measure the degree towhich norms are followed in a transitional soci-ety. Even though younger Russians employedvery different strategies than their older coun-terparts, the young conform to a well-knownnorm of sharing equally when it comes to theultimatum game.

Generally the ultimatum game is a use-ful, well-established tool to understand bilateralbargaining. Political scientists have made con-siderable use of the Baron & Ferejohn (1989)legislative bargaining model, which is nothingmore than a repeated shrinking-pie version ofthe one-period ultimatum game. Adding in-stitutional complexity to the stark bargainingarena of the ultimatum game is a fruitful avenuefor future work. Varying who is assigned to eachrole (and how) could provide insights into insti-tutional legitimacy. Manipulating the pairingsof players could shed light on implicit attitudestoward racial and ethnic others. There are nu-merous possibilities for using this well-studiedgame.

The Dictator Game

A second canonical experiment is the dictatorgame, designed to test fairness. It is similar tothe ultimatum game, except that the secondplayer has no move at the second node of thegame. The proposer, A, is given an endowmentand then allowed to split that endowmentwith the recipient, B. Player B has no choiceand must accept whatever A sends. In thissense A is a dictator; B has no action. Allplay is anonymous, thereby wiping out fear ofpostgame retaliation. The equilibrium for thissetting is obvious: A should keep everythingand send nothing to B. If something is sent,this is thought to be due to some sense offairness or an act of altruism on the part of A.

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The first such experiments were run byKahneman et al. (1986) to explicitly test un-derlying assumptions in economics in light offindings under the ultimatum game. The re-sults were straightforward in that almost threequarters of the subjects gave something to thesecond player. Forsythe et al. (1994) then testedthe same game under a variety of conditions andfound that it was impossible to move all subjectsto keeping all of their endowment.

Subsequent researchers were puzzled bythe results and pressed on several fronts. Onthe one hand, there was skepticism about A’sactions. Did A believe that there would beno postexperiment interaction? Did A believethat the choices were completely anonymous?Hoffman et al. (1994) ran a series of experi-ments that sought to strip away the fears that A’sactions could be identified. Their experimentimplemented a strong single-blind design andthen moved to a double-blind design in whichno one, including the experimenter, could pos-sibly infer what A kept. These design changeswere intended to increase the “social distance”between A and everyone else. As A’s actionswere increasingly masked from B’s, the splitsdecreased. By contrast, Eckel & Grossman(1996) made the recipient a charity—increasingthe social distance even more—and yet foundconsiderable altruistic behavior.

A recent meta-study of 129 papers that haveused the dictator game finds that dictators giveabout 28% of their endowment (Engle 2010). Inlooking at the covariates that change altruisticbehavior, Engle notes a number of experimentalparameters that affect what is sent in the dicta-tor game. Old age, having more than a singlerecipient, and making the recipient “deserving”are all predictors of positive amounts sent tothe recipient. Increasing social distance, mak-ing decisions in groups, repeating the game,moving to double-blind procedures, using stu-dent or child populations, giving the recipientan endowment, and having the dictator earnthe endowment are all predictors of decreasedamounts sent to the recipient. A number of co-variates make no difference, including the typeof incentive, the social cues that are provided,

whether there is an option to take money fromthe other player, and whether subjects are froma developing country or a “primitive” culture.In short, it remains the case that there are pos-itive contributions in the dictator game.

Political scientists have used the dictatorgame in a variety of ways. Whitt & Wilson(2007a) used the experiment as a measurementtool to understand discrimination in postwarBosnia. They found that most people adopt asimple heuristic that divides the amount whenpaired with an individual from the outgroup.Those who hold extreme nationalist views weremore willing to decrease what they send tothe outgroup. Fowler (2006), using the dicta-tor game, found that strong partisans are morealtruistic and thereby more likely to bear thecost of voting. Fowler & Kam (2007), also us-ing the dictator game, uncovered a correlationbetween sending more and participating in pol-itics and other forms of social behavior. Theyregard this as evidence that social preferencesmatter for political behavior.

Although a large number of variationsof the dictator game have been looked into,there remains plenty of scope for politicalscientists to use the same game. The coreof the dictator game involves taking a costlyaction on behalf of someone else. The dictatorgame may be useful for understanding a part ofthe representative relationship. For example,under what circumstances will a representativebe willing to sacrifice her own interests onbehalf of others? Or under what circumstancesmight a representative be willing to sacrificelocal interests for national interests? The dic-tator game may be valuable when thinking ofterrorists willing to sacrifice their own lives onbehalf of others. It may give insight into whichinstitutions foster altruistic acts. In short, anumber of questions that are of interest topolitical scientists have not been explored andcould easily use this simple experimental game.

The Trust Game

A third canonical experiment, based on the“investment game” developed by Berg et al.


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(1995), is commonly known as the trust game.In this experiment there are again two players.Both are given an equivalent endowment, andplayer A has the first move. Player A can takeany portion of the endowment and send it toplayer B. Whatever is sent is tripled in value bythe experimenter and given to B. Player B thendecides how to split the tripled amount plus theendowment. Like the ultimatum game and thedictator game, this is a one-shot game in which,once the decision is completed, the players walkaway with their earnings. Under backward in-duction, the equilibrium is obvious. Player Bwill keep her endowment and whatever was sent(and tripled). Player A, anticipating this, willrefuse to send anything at the first move. If Atrusts that B will be trustworthy, then both par-ties can be made better off.

The experiment was designed by Berget al. (1995), and they find that few indi-viduals play the Nash equilibrium underbackward induction. Johnson & Mislin (2008),in a meta-analysis of the trust game, showthat, on average, trusters send 50.8% oftheir endowment (based on 84 experiments).Trust pays (barely), in that 36.5% of what issent is returned (based on 75 experiments).Contrary to game theoretic expectations, trustis widespread and it is reciprocated.

The general findings of the trust game andits many variations are dealt with by Wilson& Eckel (2011). Their findings are consistentwith the point that individuals are generallytrusting and generally trustworthy. Yet anumber of studies point to human traits thatare related to trust and are unaccounted for bystandard game theoretic models. For example,Kosfeld et al. (2005) show that administeringthe hormone oxytocin enhances trust (but nottrustworthiness). The behavior is a response toa biological reaction. Eckel & Wilson (2008)show that skin shade has an independent effecton trusting decisions. When subjects can viewtheir partner, they send less to darker-skinnedpartners. In a similar vein, more attractivepartners are trusted more, but more attractivetrusters are penalized by trustees (Wilson &Eckel 2006). The roots of these two behaviors

have to do with stereotyping and expectations.As with the other canonical experimentsdiscussed above, there has been considerableresearch on the trust game that moves beyonda narrow sample of college students. Much ofthat work deals with population samples crossculturally (Bahry & Wilson 2004, Naef et al.2009) or with convenience samples of adults ina variety of countries and settings (Carpenteret al. 2004, Cronk 2007, Karlan 2005). In short,results from the trust game do not go away.

The concept of trust is central for politi-cal science. Many claim that trust is critical forboth the formation of social capital and for in-stitutional legitimacy. Others claim that trustamong citizens comes from stable institutions,in effect proposing the opposite causal direc-tion (for a discussion of this point, see Wilson& Eckel 2011). The investment game provides alaboratory model for settling issues of causality.As well, issues of trust go directly to the heartof the representative relationship in democra-cies. In a like vein, given that it is impossible towrite all possible contingencies into a contractor write legislation that covers all possibilities,principal–agent relationships are grounded intrust. A good deal of research has focused onthe conditions under which a principal’s trustcan be maintained. Yet few have exploited theinvestment game to explore the conditions un-der which this occurs. It might seem that thedesign of institutions is a natural place to start,but a few studies point to a perverse findingthat institutions crowd out trust (see Wilson &Eckel 2011). In a very different domain, trustis important in bargaining settings in whichactors cannot credibly commit—a hallmark ofmany agreements studied in international re-lations. In short, the issues involved with trustare important for political scientists, and thefindings from the investment game ought to beinformative.

The Public Goods Game

The final canonical experiment is widely knownin the social sciences as the public goods game,a social dilemma, or the voluntary contribution

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mechanism (in its repeated form). Often thisexperiment is used to test for social coopera-tion. In effect, this experiment is an n-personversion of a prisoners’ dilemma. The usual de-sign has more than two actors (ranging any-where from four to several hundred) who makea simultaneous decision. Each actor is given anendowment and chooses to allocate it betweena private pool and a group pool. The privatepool is similar to a private investment that re-turns at a rate of 1–1 and is returned directlyto the individual. By contrast, whatever is putinto the group pool is multiplied by the ex-perimenter and then divided equally among allmembers of the group. So long as the marginalper capita rate of return (MPCR) is less than1, then individuals have an incentive to “freeride” and contribute nothing. The dilemma isthat everyone is better off contributing every-thing if the MPCR is sufficiently large, as thatensures a socially optimal outcome. Everyonemakes more than if everyone invests in the pri-vate pool. Yet any individual always makes evenmore by investing in the private pool and freeriding off the efforts of others. Under backwardinduction, the Nash equilibrium in a one-shotgame is for everyone to invest in the privatepool.

Early experiments suggested that subjectsdid not play Nash (Dawes et al. 1977, Marwell& Ames 1979). Economists doubted these re-sults because the stakes seemed low (or in somecases nonexistent). Isaac et al. (1984) ran whathas become the canonical voluntary contribu-tion mechanism (VCM), in which the publicgoods game was repeated a number of times.Under highly controlled and paid conditions,they found much the same. Subjects did not playNash in the first period (average contributionsranged between 40% and 60%), but over time,contributions to the public good went nearlyto zero. Subsequent experiments focused onthe resilience of these results to group size andchanges in MPCR (Isaac & Walker 1988), com-munications (Sally 1995, Sell & Wilson 1991,Wilson & Sell 1997), restart effects (Andreoni1988), punishment (Fehr & Gachter 2000), andswitching to a common pool resource (Ostrom

et al. 1994). Generally the findings do not sup-port the Nash prediction.

Two influential surveys detailed the findingsfrom this literature. Ledyard (1995) elaboratedthe research up to 1995 for the Handbook ofExperimental Economics (a second edition of thisinfluential book is forthcoming). Ledyard notedmany of the persistent anomalies and suggestedroutes for future research. Ostrom (2000) tookstock of the main findings from this literature,noting seven regularities. First, subjects con-tribute 40%–60% of their endowments to thepublic good in the first round of play; second,after that round, contribution levels decay andthere is considerable free riding in the final pe-riod of play; third, beliefs about the positivecooperation of others increase one’s own co-operation; fourth, gaining experience in the ex-periment (learning) results in more, not less,cooperation; fifth, face-to-face communicationand other forms of “cheap talk” enhance levelsof cooperation; sixth, when allowed, subjectswill engage in costly punishment to sanctionthose who are not contributing; and seventh,the VCM is very sensitive to institutional fea-tures that define it (e.g., the communication orpunishment structures).

The public goods game is well known topolitical scientists. It has its foundations inOlson’s (1965) book The Logic of Collective Ac-tion. That work was central for those concernedwith interest groups and political mobilization.The free rider problem spurred a good deal ofrational choice theory and made the case thatpolitical institutions were necessary to over-come problems of collective action. Yet earlyexperimental work by political scientists calledinto question the inevitability of free riding.Orbell et al. (1984) and Dawes et al. (1986)pointed to the power of group identity in build-ing social cooperation. In later work, Ostromet al. (1994) introduced the common pool re-source version of a public goods game. In doingso, they noted that Hobbesian solutions werenot inevitable for overcoming free riding andthat groups could endogenously design theirown institutions, were skilled in communica-tions, and could deftly use punishment. More


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recently, Levine & Palfrey (2007) have useda variant of the public goods game to tacklethe question of why people turn out to votewhen voting is costly and free riding is cheap.Holding beliefs about being pivotal is cru-cial, even if those beliefs are off the mark.Habyarimana et al. (2009) effectively use thegame to test why ethnic heterogeneity leads todecreased provisioning of public goods. It ap-pears that the networks developed by coethnicsare important for ensuring monitoring andthereby ensuring the provisioning of publicgoods. These are only a few of the many studiesby political scientists on the topic.

Theoretical Innovations

Although these findings were at first ignoredby the theoretical community, the robustnessof the experimental results has motivatednew theoretical development. An early modelby Rabin (1993) proposed that individualsmight value fairness in others. For example,those who behave kindly may be rewarded withkindness and those behaving unkindly might bepunished. In effect, this model was concernedwith the intentions of the other actor. Theimportance of intention was driven home byBlount’s (1995) experiment on the ultimatumgame, in which subjects responded very differ-ently when the proposer was a human versusa computer. Rather than kindness, Levine(1998) posed a model of spitefulness. Againthe agent is aware of the actions and intentionsof another and responds in a manner thatharms the agent as well as the other. Andreoni(1990) and Andreoni & Miller (2002) turnedto whether subjects have a utility function thatis increasing with the payoffs to others.

In the late 1990s, two key models fo-cused on the distribution of outcomes,incorporating intentions, and relying onfairness and reciprocity. Fehr & Schmidt(1999) proposed a utility function in which anagent cares about not only her own payoffs butalso the payoffs of her counterpart. The utilityfunction is kinked in the sense that an agentcares about her own payoffs and cares about the

other’s payoffs relative to her own. When thecounterpart gets less than the agent, the agentmay suffer and give up some utility to make anadjustment—hence giving money to anotherin the dictator game. However, if the agentgets less than the counterpart, this comparisonmay result in negative utility—for example,leading to rejections of positive amounts in theultimatum game. Bolton & Ockenfels (2000)proposed a model of utility that is quite similar.In both cases, individuals compare their ownpayoffs with those of their counterpart and re-spond to inequalities. Generally this approachhas been noted as inequity aversion. The twomodels differ in their functional forms (Fehr& Schmidt take a linear form and Bolton &Ockenfels use a nonlinear form) and in theirbasis of social comparison (Fehr & Schmidtuse a comparison of the agent with every othercounterpart in the game, whereas Bolton &Ockenfels use a comparison of the proportionof the payoff based on the average of all otherpayoffs). The models lead to differences in pre-dictions depending on the number of players inthe game and the extent to which there are in-equalities in outcomes. These models take theexperimental data seriously and have pushed re-searchers very hard to refine their experimentsto test the robustness of these models.

There have been dozens of experiments try-ing to figure out what type of other-regardingutility function holds up. For every competi-tor, new experiments are designed to test (andbreak) a proposed model of social preferences.The classic article by Charness & Rabin (2002)began the task of testing different models of so-cial preferences. For recent variations on mod-els of social preferences, see Dufwenberg &Kirchsteiger (2004) and Engelmann & Strobel(2004). A useful starting point is the recent issueof the Journal of Economic Behavior and Organi-zation (2010, 73:1) which is partly devoted to adiscussion of the Fehr & Schmidt (1999) modelof inequity aversion.

Moving away from standard rational choicemodels may provide useful insights for politicalscientists. The idea that actors in some settingsmay impute motives to others, may be spiteful,

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or may worry about relative status is notfar fetched. Whereas many of our modelsassume that political leaders carefully weighall possible strategies before committing toconflict, anecdotal evidence points to leaderswho are driven by hatred or disdain for theircounterpart. Knowing when to incorporateother-regarding preferences into our modelswill expand our explanations. Building in suchpreferences may also help unlock who getswhich ministries under coalition bargaining. Itmay not be just a matter of seat strength andideological proximity—the shared friendshipsor enduring personal animosities may affectthe ways in which deals are reached. Socialpreferences may be useful when explaining whyvoters may vote for candidates who espousebold ideas of fairness, even though such policiesrun contrary to self interest. Incorporatingsocial preferences may also help explain whypoliticians sometimes take principled standsthat may cost them re-election. And a theoryof social preferences may be useful for under-standing the darker side of human cooperation.For example, what propels individuals tomobilize to engage in ethnic cleansing? Whatdrives individuals to kill themselves on behalfof the group? These are costly actions thatrequire coordination and cooperation.

None of these issues can be understoodwithout accounting for strategic behaviorsbetween actors. The current toolbox of gametheoretic models can get to the core issuesthat we think cause the phenomenon. Mychief concern is whether incorporating socialpreferences makes a sufficient difference forthe explanation to be worthwhile. My sense isthat building in other-regarding preferenceswill be more important when actors can easilydraw inferences about their counterpartswhen considering strategic choices. Suchpreferences will be less important in large-scalecompetitive settings in which it is difficult todraw inferences about others. I note that mystrategic considerations are quite differentwhen voting in my academic department thanwhen voting in a national election.

TIME DISCOUNTING

A second major concern for economists is howpeople treat current versus long-term gains.This is a central question for understandingsavings and investments. It would be a simplematter if people valued current assets in ex-actly the same way they value future assets. Forexample, if having $100 today is equivalent tohaving $100 in six years, then for purposes ofanalysis I could treat current and future valueas equivalent. Of course, if true for all people,then this would be a remarkably different world,in which interest and savings would be irrele-vant. Most people (and all models) assume thatpresent value is different from future value. If Iam offered $100 now, how much would it taketo get me to forego that amount now and in-stead accept some amount a year in the future?I surely would not agree to $99 a year from now.How about $101? (At current interest rates inmy savings account, this might be a good deal.)How about $110? The point at which I am will-ing to accept a future payoff in place of a presentpayoff defines the extent to which I discount thefuture. To a psychologist it may also say some-thing about my patience.

If humans are hardwired in similar ways,then we should display similar forms of dis-counting, and the standard way of representingit is through an exponential rate of discounting.Suppose that one wanted to capture the util-ity for a bundle of consumed goods over time(some vector of x’s). A convenient way of doingso is to represent this as

U t(xt, K, xT ) =T −t∑k=0

D(k)u(xt+k),

where D(k) = ( 11+r

)k.In this representation, one’s utility at some

period can be calculated, where D(k) is the dis-count function and r is an individual’s discountrate. The discount rate affects the degree towhich an individual values the future. Note thatthis model could easily allow for heterogeneityby allowing r to vary for each individual. Evenso, everyone discounts exponentially.


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The chief problem with this approach is thatpsychologists and economists have documentedmany instances in which people make inconsis-tent choices. The nature of this inconsistency isexpressed in the following way. Suppose I havea choice between $100 today and $105 tomor-row, and I prefer the $100 today. Next I amgiven the choice between $100 in six monthsand $105 in six months plus a day, and I preferthe $105. This constitutes a preference reversalthat is inconsistent with the standard form ofexponential discounting. I am not the only oneguilty of such a reversal; this result has beenreplicated many times (for a general survey, seeFrederick et al. 2002).

Brown et al. (2009) look at consumptionpatterns in the laboratory using a mechanismin which subjects live multiple lifetimes, earnperiod-by-period payments, and can experience“income shocks.” This means subjects not onlyhave to save for the future (it is possible to runout of cash before ending a lifetime), but sub-jects also get to learn from lifetime to lifetime.(In a different treatment, they were able to learnfrom one another—but for the present discus-sion, I set aside that aspect of the experiment.)The basic result, as many know from experi-ence, is that most subjects overspend early onand do not save enough. Brown et al. (2009) in-clude a second study, identical to the first exceptthat the reward medium is a beverage ratherthan money. Subjects were told not to drinkanything for four hours before coming into thelab. Upon arriving, they were given salty snacks(but no liquids), and they received 45 minutesof instructions before starting the experiment.Subjects earned points, rather than dollars, andpoints were converted to liquid. Care was takenso that subjects did not become satiated. Again,subjects overspent in the early periods and failedto save. These findings (and those by many oth-ers) point to problems with standard models ofdiscounting.

As Frederick et al. (2002) note in their sur-vey, there are numerous ways to interpret thisanomalous behavior. My own example abovemight lend support to a view of “hyperbolic”discounting (more on this below). A different

possibility is that many anomalies are due to“magnitude effects” in which individuals dis-count small sums differently from large sums.I might treat amounts in the hundred-dollarrange differently from amounts in the tens-of-thousands-of-dollars range, being more patientfor the latter than the former. Alternatively, itcould be that people exhibit greater discount-ing to avoid delay than to expedite receipt ofa good. For example, the amount I would payto receive an iPad now rather than six monthsin the future would be much less than whatI would accept if I were offered an iPad nowbut had to delay receiving it for six months.Frederick et al. (2002) illustrate a number ofother variations of these anomalies.

Like the literature on prosocial behavior,these anomalous results on time discountinghave given rise to a number of proposed theo-retical models. The most popular of these mod-els suggests hyperbolic discounting. Laibson(1997) develops what has become known as abeta-delta model designed to account for pref-erence reversals in discounting. Taking the dis-count function noted above, discounting nowhas two parameters:

D(k) =

⎧⎪⎨⎪⎩

1 i f k = 0

β

(1

1 + r

)k

f k > 0.

In essence, there is increased discounting be-tween the current period and the next, with aconstant rate of discounting thereafter. In linewith the experiment by Brown et al. (2009), thiscaptures a phenomenon of too little savings inthe earliest periods. Functional magnetic reso-nance imaging (fMRI) studies suggest differentregions of the brain are used to assess short-term and long-term decisions (McClure et al.2004), and proponents of the beta-delta modelpoint to this for support.

A second approach, a “dual-self” model, hasbeen proposed by Fudenberg & Levine (2006).They imagine two selves involved in some mul-tistage game. The “short-run” self is myopi-cally interested in each single stage of the game.The “long-run” self shares the same stage-gamepreferences as the short-run self but chooses

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some commitment device to influence the my-opic self ’s action in order to achieve a betteroutcome over the long run. An example mightinvolve a consumption problem. Suppose I amheading out to a local ice house (a Texas ver-sion of an outdoor bar). My long-run self saysI only want to spend $20 on beers. My short-run self agrees, but I know that I’ll probablywant yet another beer after my money dries up,and that before I know it, I’ll have spent $40 onbeers for myself (and no doubt my prosocial selfbuying rounds for others). My long-term selfadopts a simple device—I bring $20 in cash andleave behind my ATM card and all credit cards.This way, when my money runs out, I’m done.Fudenberg & Levine’s model deftly handles thisself-control problem and covers much the sameground as hyperbolic discounting (imagine thatthe beta component of the beta-delta functioncharacterizes the degree to which the long-termself is involved in a choice).

A number of other models have been pro-posed to deal with time inconsistency. Gul &Pesendorfer (2001) detail a model of temptationand self control. Bernheim & Rangel (2004)provide a model also dealing with temptation,using insights from addiction. All of these mod-els are designed to deal with persistent empiricalresults that show that people are inconsistent inhow they value the future.

Time discounting is not limited to eco-nomics and finance. The same concepts haveapplication in political science. First, almostall theoretical models with repeated play havesome form of time discounting in them. Asimple parameter that discounts future payoffsyields tractable solutions for infinite-horizongames. Yet such studies rely on a very stan-dard form of time discounting that does notmatch the anomalous behavior that troubleseconomists. Does it matter for our models?My sense is that it does. How much wouldtheoretical predictions for going to war varyif there is heterogeneity among actors—withsome being very foresighted and others beingextremely impatient? How do models oflegislative bargaining change when there isan influx of hyperbolic discounters into the

legislature? Are there interesting institutionalmechanisms built into political systems thatcan accommodate dual-self models?

Second, from the empirical side, the prob-lems associated with varying levels and typesof time discounting may give us insights intowhy voters prefer to hear short-term solutionsto problems or why politicians and political par-ties may only offer short-term solutions whencampaigning. It may explain why some issuespersist and others die before ever getting ontothe policy agenda. Undoubtedly there are otherpossibilities that can inform political science.

CONTEXT

The final major puzzle for behavioraleconomists pertains to context. There arethree parts to the puzzle: gains and losses,status quo bias, and framing. Kahneman et al.(1991) provides an easy, but old, entry point tothese issues. In this section, I treat each oneseparately.

Gains and Losses

When I offer advice to people designing theirfirst experiment, I caution them to avoid lossesfor subjects and I tell them to avoid a payoffof zero as one of the outcomes. Invariably, theobjection is either “in the real world peopleface losses” or “the model normalizes payoffsbetween 0 and 1 and I want to test the model.”I then advise them to scale up the payoffs andwhen asked why, my justification is that subjectsact weird in losses, and zero is a focal point thatsubjects avoid. The core of my observation in-volves loss aversion by subjects and the fact thatzero is an important reference point.

Kahneman & Tversky (1979) opened upthe discussion in economics when questioningwhether standard forms of expected utility heldup. Their 1979 article on prospect theory de-veloped a powerful critique of expected utility(and some of its weighted variants), proposed autility function in which individuals were riskseeking in gains and risk avoiding in losses,and illustrated the issues with a large number


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of hypothetical examples. This article set off asubstantial and ongoing debate in economicsover the understanding of risk (for reviews, seeCamerer 1995, 2000; Laibson & Zeckhauser1998; Starmer 2000).

Grether & Plott (1979) did not believe theKahneman & Tversky results. They thoughtsuch an anomaly could be easily wiped outusing the appropriate experimental design.They complained that the Kahneman &Tversky results were supported by experimentsusing hypothetical statements. This meantthat the subjects had little incentive to thinkhard about the problem. In a clever set oflaboratory experiments with money on thetable, Grether & Plott (1979) found the samephenomenon—preference reversals. “Needlessto say,” they admitted, “the results we obtainedwere not those expected when we initiated thisstudy” (p. 634). This spawned a large numberof experiments designed to do away with theproblem. But it has not gone away—althougha recent survey (Berg et al. 2010) points outthat incentives may limit reversals.

Recent work has tried to “marry” ex-pected utility and prospect theory. Harrison &Rutstrom (2009) conducted an experiment overmany gambles in a gains frame, a losses frame,and a mixed gains and losses frame. They explic-itly account for subject heterogeneity (sex, race,and age differences) and point out that bothmodels (expected utility and prospect theory)survive. However, both models appear to op-erate in conjunction with heterogeneous traitsbrought into the lab and in specific task do-mains. Loomes et al. (2010) take a differentstrategy by designing an experiment to see ifmarket forces erode problems of preference re-versals. The prediction is that the market dis-ciplines those who do not adhere to standardassumptions. However, they, too, find that pref-erence reversals remain.

Little of this abbreviated discussion shouldsurprise political scientists. We have long beenaware of prospect theory. Influential surveys byLevy (2003), McDermott (2004), and Mercer(2005) point to its importance for internationalrelations. They regard risk-seeking behavior as

important for explaining military gambles. Onthe other hand, risk aversion may help explainthe relative stability of international systems.As Mercer (2005) makes clear, prospect theoryhas not made much progress in other fieldsof political science. Political economists (whocommonly assume risk neutrality in models)could benefit from accounting for differentresponses to gains and losses. Political psy-chologists ought to be interested in differentialcognitive processing under gains and losses. Asto the latter, recent work by McDermott et al.(2008) leverages a foraging model to groundprospect theory in evolutionary theory, therebyposing a potential explanation for the underly-ing psychological mechanisms driving differentresponses to gains and losses. Because it seemslikely that risk preferences are dramaticallydifferent in gains and losses domains, thesedifferences ought to be accounted for in ourmodels. Perhaps a sticking point for many isthat the reference point on which such modelsare dependent is incompletely understood.

Status Quo Bias

A second puzzle has been characterized as thestatus quo bias or the endowment effect. Theproblem was originally noted by Knetsch &Sinden (1984) and made more widely knownby Kahneman et al. (1990). The puzzle is bestrepresented by a classic experiment from thislatter article. Subjects were randomly assignedto receive a coffee mug or not. After handlingtheir coffee mug, subjects were asked how muchthey were willing to accept to part with it. Thosewithout a mug were asked how much they werewilling to pay for one. The median price forsellers was $5.75 and the median price for buy-ers was $2.25. The difference could be dueto what experimentalists call a “wealth effect,”so another experiment was run in which therewere buyers, sellers, and choosers. The two for-mer roles were the same as before, while thechoosers made a set of choices between whetherto accept a specific amount of cash or accept themug. Under these conditions, the median pricefor sellers was $7.12, for buyers it was $2.87, and

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for choosers it was $3.12. The interesting find-ing is that subjects overvalue the item that theyhave in hand. This endowment effect persistsin many environments, providing a basis for theold adage “a bird in the hand is worth two in thebush.” In effect, individuals value those thingsthat they hold.

The endowment effect is not limited toadults. Harbaugh et al. (2001) demonstrate thatthe endowment effect carries over to children.Chen et al. (2006) provide evidence of it in ca-puchin monkeys. A quasi-field experiment overthe trading behavior of sports card memora-bilists indicates a preference for the status quo,although the effect diminishes with the experi-ence of the trader (List 2003). A recent modelby K´oszegi & Rabin (2006) points out that thereference point held by actors is crucial, and thatmore experienced actors may have better esti-mates of the likelihood of retaining the endow-ment. In short, the endowment effect seems topersist. With experience it tends to dissolve, butas DellaVigna (2009) points out, even tradershold onto some stocks too long.

For political scientists, accounting for sta-tus quo bias can be quite important. It can beuseful when considering why incumbents en-joy an advantage with respect to voters. Forexample, Patty (2006) uses status quo bias tohelp model congressional midterm losses forthe president’s party. Status quo bias providesinsight into the stickiness of legislation. It mighthelp explain why institutional rules are not con-tinually rewritten. Status quo bias may help ex-plain why trade agreements are so tortuous andwhy interest groups can thrive once they gain afoothold. In short, this is a puzzle that politicalscientists are familiar with but rarely include inmodels.

Framing

The final puzzle involves framing and waspointed out by Tversky & Kahneman (1981).The puzzle is tied with loss aversion, sincemany of the examples (and experiments) changethe frame from gains to losses. However, indi-viduals should not switch their choices when

given inconsequential changes in the descrip-tion of the choices. In the classic example, sub-jects are presented with the possibility of an un-specified “Asian disease” that is expected to kill600 people and are asked to choose betweentwo different policies. If policy A is chosen,200 people will be saved; if policy B is chosen,there is a one-third chance that 600 people willbe saved and a two-thirds chance that no peoplewill be saved. Typically people prefer policy A,which indicates risk aversion. Another group ofrandomly selected subjects is given the equiv-alent policy options, but framed in a differentmanner. If policy A∗ is adopted, 400 people willdie; if policy B∗ is adopted, there is a one-thirdchance that no one will die and a two-thirdschance that 600 people will die. Subjects nowfavor policy B∗, the risk-seeking policy. Asidefrom the framing, the pairs of policies (A,A∗)and (B,B∗) are equivalent. Yet behavior varieswith the frame.

Fehr & Goette (2007) find strong evidenceof reference effects that can easily be manipu-lated through framing. Their field experimentwith bicycle couriers in Switzerland has the au-thors manipulating wages and using lotteriesthat are similar to the framing problem notedabove (but with cash incentives). They find evi-dence of switching behavior in the lotteries andlink this to labor and effort decisions.

Framing effects are not unusual anomaliesfor political scientists. The discipline was intro-duced to the problem by Quattrone & Tversky(1988). That article was presented as a chal-lenge to rational choice theory and providedevidence from experiments in which subjectsmade choices based on hypothetical settings—very similar to those of Tversky & Kahneman(1981), mentioned above. However, it had amuted impact on game theorists in political sci-ence. This does not mean that framing is unfa-miliar to the discipline. Chong & Druckman(2007) catalogue the numerous ways in whichframing effects have been exploited: detailinghow issues are presented to the public, howsupport for national institutions varies underdifferent frames, and how competitive framesplay out in public opinion. Given that interest


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groups and politicians are constantly compet-ing to bring issues to the fore, it is no surprisethat framing should be important in politicalscience.

Generally, the anomalies relating to con-text have been at the forefront for politicalscientists. A brief sampling of the politicalscience literature turns up overviews of contexteffects for shaping public opinion (Druckman& Lupia 2000, Jones 1999, McDermott 2001);concerted efforts at understanding prospecttheory and its implications for modeling riskin international relations (Boettcher 2004,Levy 2003, Mercer 2005, O’Neill 2001); and agood deal of work on framing and its relevancefor political decision making (Baumgartner &Mahoney 2008, Druckman 2004, Druckman &McDermott 2008). In short, political sciencehas absorbed these lessons and applied them toits own distinct problems. However, empiricalwork has outpaced theoretical attention tomany of these issues.

Psychological Irregularities

Outside the trinity of major behavioral puzzlesfor economists, there is a cluster of psycho-logical mechanisms of concern. Whether theseare considered heuristics or psychologicalpeculiarities does not matter for the purposesof this review. They are well known, welldocumented, and pose problems for standardeconomic theory. I treat four problems:overconfidence, levels of thinking, emotions,and status. There are others. I could just aseasily have chosen probability over- and under-weighting (it turns out that people are not verygood at judging low-probability events; weoverweight the likelihood that the event willoccur). Or I could have turned to projectionbias, in which people use their current situationto project future action (“I’m in good healthnow, so I plan on being in good health in thedistant future”). Or I could have detailed neu-roeconomics, which as a subfield tackles manyof these issues [for an interesting discussion ofthe value of neuroeconomics, see the specialissue of Economics and Philosophy, 24(3), 2008].

Overconfidence

Psychologists have long noted the Lake Wobe-gon effect, named after Garrison Keillor’s townin which “all the children are above average.”Svenson (1980), for example, reported that80% of all drivers believed their driving abilitywas above average. For behavioral economics,the question is whether this finding persists inthe face of market structures that ought to driveout overconfidence. There appears to be plentyof evidence that entrants into markets areoverconfident about their likelihood of success,investors are overconfident about their abilityto read the market, and males are overconfidentabout their skills in competitive settings.

Camerer & Lavallo (1999) began muchof the discussion in economics by examin-ing whether excess market entry can partly beexplained by overconfidence in one’s ability(a “competitive blindspot”). Camerer & Lavalloexperimentally manipulated the payoffs in dif-ferent markets, elicited whether subjects choseto enter each market, and then used a triviaquiz to determine performance. Performancewas measured after entry decisions were made.As expected, overconfidence abounded, withsubjects entering more frequently into mar-kets when performance was based on their ownactions (compared with a random assignmentcondition). Numerous other studies have repli-cated this finding about overconfidence in abil-ities (see, e.g., Clark & Friesen 2009).

In a survey of the gender gap in math abil-ities, Niederle & Vesterlund (2010) point toseveral experiments demonstrating overconfi-dence by males in competitive settings. In par-ticular, they note an experiment in which sub-jects were given math problems to solve usingtwo different payment schemes. The first wasa piece-rate setting, in which a fixed amountwas paid for each correctly solved problem.The second setting was competitive, with theperson who got the most correct answers intheir four-person group (two males and two fe-males) being paid a winning amount and ev-eryone else receiving nothing. In both pay-ment settings, males and females, on average,

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correctly answered the same number of mathproblems. Having experienced both paymentsettings, but not being informed of their perfor-mance or rank in either setting, subjects wereasked which payment scheme they wanted forthe next math task. Almost three quarters ofthe men and only 37% of the women chose thecompetition setting. To get at beliefs, subjectswere asked how they thought they ranked inthe competition setting and were paid for a cor-rect guess. Seventy-five percent of the men and43% of the women guessed they performed thebest in the competition. Although both men andwomen are overconfident in their abilities, menare more so. No matter how hard the authorstry to get rid of this gap, they are unable to do so.

Overconfidence is a well-known problem.It can lead to overbidding in auctions, it canexplain too many entrants into market, and itmight explain some differences between malesand females in competitive settings. For po-litical scientists, overconfidence may be usefulfor explaining why some candidates persist incampaigning even when they have no chance ofelection. The same underlying mechanism mayexplain campaign donations to a losing cause(and I conjecture there will be pronounced gen-der differences in those donations). In any com-petitive political setting, seemingly irrationalbehavior may be the result of overconfidenceby one or more individuals. This may give in-sight into candidates who pour tens of millionsof their own wealth into running for office. In avery different setting, overconfidence may helpexplain why some leaders enter into conflictsthey cannot win. Sometimes “resolve” may sim-ply be misplaced confidence.

Levels of Thinking

Most standard models assume economic agentsare not doomed to myopia and, at a minimum,are capable of thinking ahead. This implies thatbackward induction for any structured gameought to be easy. Certainly top-rated chessplayers are capable of thinking many stepsahead, but it turns out that most people are notvery good at multistage strategic reasoning.

The classic example is the “beauty pageant”game, in which there is an array of contestantsand the task is to pick the average of the choicesof the judges. Assuming there are 100 contes-tants evenly spread on a number line rangingfrom 0 to 99, whom should any individual pick?A judge with no sophistication or beliefs aboutothers can simply choose a random value. Thiswould be someone with zero-order beliefs. Aslightly more sophisticated judge might form(first-order) beliefs about the behavior of otherjudges and generate a best-response choice.Another may form second-order beliefs aboutthe first-order beliefs of others and so on. Sup-pose my zero-order beliefs entail a guess of theaverage of the contestants on the number line(choosing between the 49th and 50th contes-tants). A first-order belief might anticipate thisand lead me to choose some value below this,while also anticipating the response by otherfirst-order beliefs. This continues for higher-order beliefs. If everyone holds higher-orderbeliefs, then the choice is the contestant at 0.

Nagel (1995) tests a variation of this game todetermine whether there are levels of reasoningamong subjects. Subjects repeat the task severaltimes and are informed about the outcomefollowing each play. She finds evidence forheterogeneity in the population but also findsthat subjects learn with repetition. The modalsubject engages in level 2 thinking, and the bulkof the subjects are at level 3 thinking or below.Ho et al. (1998) extend this study and find sim-ilar results. There is a large number of peoplewho begin with level 0 thinking, but learn.Those who are sophisticated (beginning atlevel 1 or above) best respond to level 0 players.Everyone learns, but not everyone adjusts asfully foresighted agents (see also Costa-Gomes& Crawford 2006, Coricelli & Nagel 2009).

Such findings could explain the dearthof results on strategic voting in politicalscience. Although theoretical models suggestthe importance of strategic voting, my ownexperimental data point largely to sincerevoting (Wilson 1986, Wilson & Pearson 1987).McKelvey & Palfrey (1992) find little evidencefor higher-level strategic thinking in the


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“centipede game.” In this game, players havealternating moves in which they can eitherstop the game or pass to their counterpart. Ateach choice node, there is an increasing piewith an asymmetric split (e.g., a [$4,$1] split atthe first node and a [$2,$8] split at the secondnode and so forth). The stakes get very highat the last node of the game, but subjects neverget there. McKelvey & Palfrey (1992) do findstrong evidence for best-response behavior tovarious levels of thinking, but little higher-levelstrategic behavior.

The depth of strategic thinking should becritical for the ways we model different environ-ments. For phenomena in which many skilledagents contribute to strategic choice, fore-sighted strategic reasoning seems appropriate.Here I am thinking of decisions made by nation-states in the international arena when there isplenty of time. Diplomatic, military, and lo-cal political considerations will contribute asdecision makers work through many levels ofreasoning. By contrast, decisions made undertime pressure by individuals with little infor-mation and little experience may be more fruit-fully modeled as low-level strategic reasoning.For example, voting on obscure, nonpartisan,local candidates can be treated in this fashion.Overall our models would benefit from askingwhether predicted outcomes would change aswe shift from higher- to lower-order beliefs.

Emotions

Elster (1998) provides an important survey ofthe manner in which emotions may influenceeconomic decisions. In particular, he pointsto the role of envy and indignation in moti-vating rejections in the ultimatum game andthe place of love, hate, revenge, and contemptin a number of other settings. Prior work byFrank (1988) had established a basis for emo-tion in bargaining models. For Frank, difficult-to-disguise emotions like anger have the valueof demonstrating commitment to a course ofaction.

It is well established in psychology that cer-tain kinds of emotion can focus individuals onnarrow goals and outcomes. Whitt & Wilson

(2007b) use a one-shot public goods game to ex-amine levels of cooperation among HurricaneKatrina evacuees housed in rescue shelters. Allof the participants were very stressed (havingbeen evacuated from New Orleans the weekprior), but the most stressed were peoplewhose family members were still missing.Those individuals contributed the least to thepublic good, and their behavior was consistentwith what is expected of people who are highlyfearful. Likewise, Eckel et al. (2008) reportrisk-seeking behavior among Katrina evacueesthat reflects similar emotional reactions. Inboth of these articles, games drawn fromexperimental economics were used to measurebehavior in light of varying emotional states.

Xiao & Houser (2005) use the ultimatumgame to determine whether providing subjectsan emotional outlet changes behavior. Whilea control group of subjects played a standardultimatum game, those in the treatment groupwere given the opportunity to write a messageto the proposer. The findings were clear. Whengiven an opportunity to express an emotion,there were fewer rejections of low offers. Xiao& Houser (2005) interpret this to mean thatcostly punishment (rejections in the controlcondition) is used to express negative emotion.When another option is available to expressemotion, costly punishment is not used. Thereare a handful of other articles on emotion andbehavior (e.g., Fong 2007). However, althoughemotions are recognized as important foreconomic decision making, to this point fewresearchers have followed up on the promise.

An exception is the enormous literature onthe topic of happiness. Much of that work isdriven by responses to survey items concerninghappiness (Clark et al. 2008). This voluminoussurvey literature often compares the aggregatelevels of happiness across nations or places. Thefindings correlate everything from increasingcountry-level gross domestic product (whichimproves general happiness) to positive incomeshocks (which improve individual happinessonly in the short run). Generally, happiness istaken to be an emotional mood and serves as aproxy for well-being.

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Emotion has long been a part of politicalscience. Work on “affective intelligence”(Marcus 2000) details how individual decisionmakers are more or less attentive to infor-mation depending on their affective state.Others demonstrate how people under threatevaluate candidates and policy issues (Brader2005, Huddy et al. 2005). Opportunities foradditional research abound. How elites useemotional frames to mobilize citizens is not wellunderstood. The circumstances under whichemotions are amplified within groups need to beaddressed. For example, it appears that my fear-fulness elevates when I am surrounded by otherswho are also fearful. Emotions are very mucha part of our genetic makeup. In us, as in otheranimals, emotions dictate the four F’s: fighting,fleeing, feeding, and mating. This message hasnot been lost on those concerned with biolog-ical foundations of politics (Oxley et al. 2008).

Status

Humans often look to others to decide whomto emulate or imitate. The decision of whomto follow is not random and is often associ-ated with status. Status, of course, can be con-ferred for many reasons (Henrich & Gil-White2001, Webster & Hysom 1998), but ordinar-ily it should have little economic consequence.However, as Mathur et al. (1997) show, whenMichael Jordan announced he was returningto the NBA following a brief retirement anda stint as an unsuccessful baseball player, hisclient companies showed a 2% increase in stockprices, valued at over one billion dollars. Few ofus are likely to “be like Mike.” However, appar-ently many of us looked to him.

Although status should not matter foreconomic decisions, there is a good deal ofevidence that it does. Ball et al. (2001) providea beautifully designed experiment to test theeffect of status in a market. They use a box-design market in which all sellers have the samecost and all buyers have the same reservationvalue. An equal number of units is demandedand supplied, which leads to a vertical overlapin the demand and supply curves. They use an

oral double auction institution, which pushesthe market to a uniform price. The equilibrium(market price) can be anywhere between thesellers’ cost and buyers’ reservation value.Status in this experiment was randomlyawarded. Prior to beginning the experiment,half the group was given a gold star and thosewithout gold stars were told to applaud thosewith. In one treatment, all those with goldstars (the high-status group) were assigned tobe buyers, which everyone knew. In the othertreatment, those with gold stars were assignedto be sellers. Ball et al. find that when buyershave higher status, the market price is lowerthan when the sellers have higher status. Onaverage, high-status players earn 11.4% more.Markets should not be responsive to status, butthey are. (For more experiments with bargain-ing games and status, see Ball & Eckel 1998,Eckel & Wilson 2007, Eckel et al. 2010.) In theend the question is why are people attentive tostatus: for reasons of imitation or for deference?

Status is a central concern for several issuesin political science. A clear understanding ofleadership eludes us. We can point to specificleaders, but we are hard pressed to predict whowill become a leader or to prescibe how toenhance one’s leadership. Many of these issuesseem to be tied to status. Status, of course, is asignal, and it would be valuable to know howthat signal is sent and read. Political scientistsoften talk about things that confer status onindividuals, groups, or even nations. In somemanner, having status leads to others respond-ing in predictable ways. As with economicdecisions, status should not affect political out-comes. It does, and it remains a puzzle for us.

In summary, numerous psychologicalanomalies pervade market economies—farmore than have been touched on here. Thesesame anomalies pervade political science.

MOVING BEYOND ANOMALIES

Political scientists are fortunate in that weare not bound to a particular approach tounderstanding the core issues of politics.Rational choice, social choice, and game theory


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provide important insights into political behav-ior, but political scientists have not been afraidto borrow from psychology, sociology, and an-thropology as well. What does behavioral eco-nomics mean for political science?

It is crucial to understand whether thereare social preferences. Here the questions aremany, complex, and perhaps daunting. Dopeople have a taste for fairness? Are peoplewilling to punish others (at a cost to themselves)when norms are violated? Are people attentiveto hierarchy and their position in it? Do peoplehave a propensity to trust others? Are peoplenaturally cooperative (and to what end: to helpthe group or to destroy another group)? Thesequestions raise a number of issues. First, aremany of these behaviors innate? Do humanshave a propensity toward such behavior? If so,such dispositional traits should be helpful ingetting the assumptions of our models right.A second issue is the extent to which thesetastes are easily manipulated by leaders orredirected by institutional design. Causally,it may be that a taste for trust is a necessarycondition for stable political institutions, orperhaps the causal relationship runs the otherway. These and other issues need to be sortedout.

How can findings on time discounting bebrought into political science? This is a seriousquestion not only for theorists (who often relyon fairly simple models of time discounting) butalso for empiricists. After all, voting and cam-paigning are importantly tied to promises aboutthe future and how citizens view the future.

The puzzle of context is not so puzzling forpolitical scientists. We are used to these prob-lems and have accounted for them in a rich arrayof models and studies. However, as a discipline,we should not forget that gains and losses, sta-tus quo bias, and framing pose challenges tostandard models and need to be accounted for.

There are a large number of psychologicalheuristics to consider when we model behavioror empirically test predictions. Not all of theseheuristics will matter—many may be domainspecific. However, they are important puzzlesthat are grounded in human psychology. In the

next ten years cognitive neuroscience may pro-vide important insights to the discipline, muchas microeconomic theory did 30 years ago. I donot expect that the fundamentals of rationalitywill disappear, but rather that systematic find-ings from cognitive neuroscience will informour simplified models of human behavior.

We know that peer effects help mobilizeindividuals. But we remain unclear about themechanisms that push people to vote on thisbasis. They may have to do with imitation, so-cial identity, overweighting the fear of socialhumiliation—the question is important, but re-mains unsolved.

Emotion needs to be brought into the studyof social behavior. So far, political science hasapplied emotion to individuals. We have a goodsense of what anxiety and fear mean for individ-ual behavior. Yet, we suspect that many aspectsof political life—including mobilizing peopleto harm others—require an emotional spark. Itmay be that such a spark works through anger,disgust, or fear. However it works, these emo-tions most likely are compounded by social in-teraction. Figuring out this mechanism will notbe easy.

Finally, political science should take note ofwhat has driven behavioral economics and im-itate it. Many of the compelling findings havebeen derived from the laboratory. Skeptical re-searchers have created inventive experimentaldesigns to understand where standard theorybreaks down. The end is not to demonstratethat the theory fails. Rather, the aim is topinpoint the circumstances under which itfails. Behavioral economics is not awash withresearchers seeking to dismantle the standardmodel. Instead, those researchers begin withthe premise that the model provides insightand are puzzled by their findings. This senseof puzzlement and honesty in designing newexperiments to test the boundaries of failurehave drawn the formal theorists into trying toexplain the lapses. There are positive signs thatthe laboratory will be used more frequently foranswering fundamental questions in politicalscience. It will be important to engage formaltheorists with those findings.

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DISCLOSURE STATEMENT

The author is not aware of any affiliations, memberships, funding, or financial holdings that mightbe perceived as affecting the objectivity of this review.

ACKNOWLEDGMENTS

I apologize for all the great studies that I had to omit from this survey. From the huge field ofbehavioral economics, I had to choose what I think might be of most value to political scientists. Iowe a profound debt of gratitude to Catherine Eckel, who, many years ago, got me interested inthese issues and who has explored some of them with me in our joint work. Conversations withCharlie Holt, Lin Ostrom, Charlie Plott, Enrique Fatas, and Lise Vesterlund have made me thinkhard about these issues. None of the above should be blamed for what I have forgotten, but theyshould be credited with what I have remembered.

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PL14-frontmatter ARI 11 April 2011 17:22

Annual Review ofPolitical Science

Volume 14, 2011Contents

A Life in Political ScienceSidney Verba � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � i

Leadership: What It Means, What It Does, and What We Want toKnow About ItJohn S. Ahlquist and Margaret Levi � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 1

Examining the Electoral Connection Across TimeJamie L. Carson and Jeffery A. Jenkins � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �25

Presidential Appointments and PersonnelDavid E. Lewis � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �47

Understanding the 2007–2008 Global Financial Crisis: Lessons forScholars of International Political EconomyEric Helleiner � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �67

Presidential Power in WarWilliam G. Howell � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �89

The Politics of Regulation: From New Institutionalism to NewGovernanceChristopher Carrigan and Cary Coglianese � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 107

The New Judicial Politics of Legal DoctrineJeffrey R. Lax � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 131

The Rhetoric Revival in Political TheoryBryan Garsten � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 159

The Rhetoric of the Economy and the PolityDeirdre Nansen McCloskey � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 181

The Contribution of Behavioral Economics to Political ScienceRick K. Wilson � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 201

The Causes of Nuclear Weapons ProliferationScott D. Sagan � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 225

Network Analysis and Political ScienceMichael D. Ward, Katherine Stovel, and Audrey Sacks � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 245

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PL14-frontmatter ARI 11 April 2011 17:22

The Big Five Personality Traits in the Political ArenaAlan S. Gerber, Gregory A. Huber, David Doherty, and Conor M. Dowling � � � � � � � � � � 265

ClientelismAllen Hicken � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 289

Political Economy Models of ElectionsTorun Dewan and Kenneth A. Shepsle � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 311

Modeling Dynamics in Time-Series–Cross-Section PoliticalEconomy DataNathaniel Beck and Jonathan N. Katz � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 331

Voting TechnologiesCharles Stewart III � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 353

Indexes

Cumulative Index of Contributing Authors, Volumes 10–14 � � � � � � � � � � � � � � � � � � � � � � � � � � � 379

Cumulative Index of Chapter Titles, Volumes 10–14 � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � 381

Errata

An online log of corrections to Annual Review of Political Science articles may be foundat http://polisci.annualreviews.org/

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the contribution of behavioral economics to political science

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